The induction motor is the most popular motor in energy conversion and industrial drive systems. This popularity is due to its robustness, low cost, and easy maintenance. Electrical, mechanical, thermal, magnetic and environmental stresses cause faults in the induction motor during the operation process. One of the challenging topics for many researchers is the development of a reliable efficient induction motor fault diagnostic techniques. The broken rotor bar fault is one of the critical faults that need to be early detected due to its sudden severe damages. The objective of this study is to present a survey of existing broken rotor bar fault detection techniques with new classification based on fault signature. Various monitoring conditions and signal processing techniques are considered for the detection process. A comprehensive list of references is reported for each fault signature and classified based on (i) loading level, (ii) the number of broken bars, (iii) validation and (vi) signal processing.

Photovoltaic (PV) technology is gaining much interest as a clean, sustainable, noise-free source of energy. However, the non-linear behavior of PV modules and their dependency on varying environmental conditions require thorough study and analysis. Many PV modeling techniques have been introduced in the literature, yet they exhibit several complexity levels for parameter extraction and constants estimation for PV power forecast. Comparatively, a simple, accurate, fast, and user friendly PV modeling technique is proposed in this paper featuring the least computational time and effort. Based on function representation of PV curves’ available in PV datasheets, an empirical mathematical equation is derived. The proposed formula is considered a generic tool capable of modeling any PV device under various weather conditions without either parameter estimation nor power prediction. The proposed model is validated using experimental data of commercial PV panels’ manufacturers under various environmental conditions for different power levels. The obtained results verified the effectiveness of the proposed PV model.